Condition-monitoring (CM) based fault diagnosis of rotating machinery [1]}, [2]} is widely used in industry to optimize equipment availability, uniformity of product characteristics and safety in the work environment, and to minimize production losses and material waste.
In process industry, this typically requires human expert analysts with years of training and detailed knowledge about the operational states, functional roles and contexts of the machines being monitored.
Due to growing demands on production efficiency and the vast amounts of data consequently generated in modern CM systems, automated fault diagnosis systems [3]} are required to assist human analysis through alarms and policy recommendation.
Important tasks for the automated system are fault detection and classification to generate alarms and filter data, and fault severity estimation to predict remaining useful life and recommend policy options.
Existing automated systems are mainly based on expert systems [4]}, with a knowledge-base derived from physical properties of analysed components, and a rule-based inference engine with local thresholds set by experts [5]}.
In the case of vibration measurements of rotating machinery, signal processing and kinematics based condition indicators are commonly used as knowledge-bases [6]}, [7]}, [8]}.
Intelligent fault diagnosis (IFD) [9]} has been proposed to enhance the automated systems by inferring fault characteristics directly from process or lab data through learning based methods.
Improving existing models is vital to meet the increasing demands on CM systems to improve production and equipment life cycle efficiency in process industry [10]}, [11]}, and the machine CM market is estimated at $2.6 billion with a compound annual growth rate estimation of 7.1%https://www.marketsandmarkets.com/Market-Reports/machine-health-monitoring-market-29627363.html.
For example, improved IFD algorithms can contirbute to: reducing the number of unnecessary interventions; facilitating remanufacturing of components [12]}; optimising maintenance schedules; and enabling analysts to focus on qualified preventive tasks.
